A key point in FL is the way specialized models are aggregated. In the case of deep learning, two families of algorithms can be considered. The first strategy is to emphasize generalization. The aggregation algorithm considers local models in their entirety and builds a new model that potentially calls into question all layers and all weights associated with neurons. This approach is shown in FedAvg [1]} and FedMA [2]} algorithms. The second strategy focuses more on client specialization. Here, the algorithm does not question certain parts of the local models. Precisely, only the base layers are sent to the server for generalization, while the last layers are kept unchanged. This approach is used in FedPer algorithm [3]}.
